89 research outputs found
Generalizing to Out-of-Sample Degradations via Model Reprogramming
Existing image restoration models are typically designed for specific tasks
and struggle to generalize to out-of-sample degradations not encountered during
training. While zero-shot methods can address this limitation by fine-tuning
model parameters on testing samples, their effectiveness relies on predefined
natural priors and physical models of specific degradations. Nevertheless,
determining out-of-sample degradations faced in real-world scenarios is always
impractical. As a result, it is more desirable to train restoration models with
inherent generalization ability. To this end, this work introduces the
Out-of-Sample Restoration (OSR) task, which aims to develop restoration models
capable of handling out-of-sample degradations. An intuitive solution involves
pre-translating out-of-sample degradations to known degradations of restoration
models. However, directly translating them in the image space could lead to
complex image translation issues. To address this issue, we propose a model
reprogramming framework, which translates out-of-sample degradations by quantum
mechanic and wave functions. Specifically, input images are decoupled as wave
functions of amplitude and phase terms. The translation of out-of-sample
degradation is performed by adapting the phase term. Meanwhile, the image
content is maintained and enhanced in the amplitude term. By taking these two
terms as inputs, restoration models are able to handle out-of-sample
degradations without fine-tuning. Through extensive experiments across multiple
evaluation cases, we demonstrate the effectiveness and flexibility of our
proposed framework. Our codes are available at
\href{https://github.com/ddghjikle/Out-of-sample-restoration}{Github}
Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization
Federated Domain Generalization aims to learn a domain-invariant model from
multiple decentralized source domains for deployment on unseen target domain.
Due to privacy concerns, the data from different source domains are kept
isolated, which poses challenges in bridging the domain gap. To address this
issue, we propose a Multi-source Collaborative Gradient Discrepancy
Minimization (MCGDM) method for federated domain generalization. Specifically,
we propose intra-domain gradient matching between the original images and
augmented images to avoid overfitting the domain-specific information within
isolated domains. Additionally, we propose inter-domain gradient matching with
the collaboration of other domains, which can further reduce the domain shift
across decentralized domains. Combining intra-domain and inter-domain gradient
matching, our method enables the learned model to generalize well on unseen
domains. Furthermore, our method can be extended to the federated domain
adaptation task by fine-tuning the target model on the pseudo-labeled target
domain. The extensive experiments on federated domain generalization and
adaptation indicate that our method outperforms the state-of-the-art methods
significantly.Comment: Accepted by AAAI 202
Joint Correcting and Refinement for Balanced Low-Light Image Enhancement
Low-light image enhancement tasks demand an appropriate balance among
brightness, color, and illumination. While existing methods often focus on one
aspect of the image without considering how to pay attention to this balance,
which will cause problems of color distortion and overexposure etc. This
seriously affects both human visual perception and the performance of
high-level visual models. In this work, a novel synergistic structure is
proposed which can balance brightness, color, and illumination more
effectively. Specifically, the proposed method, so-called Joint Correcting and
Refinement Network (JCRNet), which mainly consists of three stages to balance
brightness, color, and illumination of enhancement. Stage 1: we utilize a basic
encoder-decoder and local supervision mechanism to extract local information
and more comprehensive details for enhancement. Stage 2: cross-stage feature
transmission and spatial feature transformation further facilitate color
correction and feature refinement. Stage 3: we employ a dynamic illumination
adjustment approach to embed residuals between predicted and ground truth
images into the model, adaptively adjusting illumination balance. Extensive
experiments demonstrate that the proposed method exhibits comprehensive
performance advantages over 21 state-of-the-art methods on 9 benchmark
datasets. Furthermore, a more persuasive experiment has been conducted to
validate our approach the effectiveness in downstream visual tasks (e.g.,
saliency detection). Compared to several enhancement models, the proposed
method effectively improves the segmentation results and quantitative metrics
of saliency detection. The source code will be available at
https://github.com/woshiyll/JCRNet
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
With a focus on abnormal events contained within untrimmed videos, there is
increasing interest among researchers in video anomaly detection. Among
different video anomaly detection scenarios, weakly-supervised video anomaly
detection poses a significant challenge as it lacks frame-wise labels during
the training stage, only relying on video-level labels as coarse supervision.
Previous methods have made attempts to either learn discriminative features in
an end-to-end manner or employ a twostage self-training strategy to generate
snippet-level pseudo labels. However, both approaches have certain limitations.
The former tends to overlook informative features at the snippet level, while
the latter can be susceptible to noises. In this paper, we propose an Anomalous
Attention mechanism for weakly-supervised anomaly detection to tackle the
aforementioned problems. Our approach takes into account snippet-level encoded
features without the supervision of pseudo labels. Specifically, our approach
first generates snippet-level anomalous attention and then feeds it together
with original anomaly scores into a Multi-branch Supervision Module. The module
learns different areas of the video, including areas that are challenging to
detect, and also assists the attention optimization. Experiments on benchmark
datasets XDViolence and UCF-Crime verify the effectiveness of our method.
Besides, thanks to the proposed snippet-level attention, we obtain a more
precise anomaly localization
Prototype-guided Cross-task Knowledge Distillation for Large-scale Models
Recently, large-scale pre-trained models have shown their advantages in many
tasks. However, due to the huge computational complexity and storage
requirements, it is challenging to apply the large-scale model to real scenes.
A common solution is knowledge distillation which regards the large-scale model
as a teacher model and helps to train a small student model to obtain a
competitive performance. Cross-task Knowledge distillation expands the
application scenarios of the large-scale pre-trained model. Existing knowledge
distillation works focus on directly mimicking the final prediction or the
intermediate layers of the teacher model, which represent the global-level
characteristics and are task-specific. To alleviate the constraint of different
label spaces, capturing invariant intrinsic local object characteristics (such
as the shape characteristics of the leg and tail of the cattle and horse) plays
a key role. Considering the complexity and variability of real scene tasks, we
propose a Prototype-guided Cross-task Knowledge Distillation (ProC-KD) approach
to transfer the intrinsic local-level object knowledge of a large-scale teacher
network to various task scenarios. First, to better transfer the generalized
knowledge in the teacher model in cross-task scenarios, we propose a prototype
learning module to learn from the essential feature representation of objects
in the teacher model. Secondly, for diverse downstream tasks, we propose a
task-adaptive feature augmentation module to enhance the features of the
student model with the learned generalization prototype features and guide the
training of the student model to improve its generalization ability. The
experimental results on various visual tasks demonstrate the effectiveness of
our approach for large-scale model cross-task knowledge distillation scenes
Operating Conditions of Hollow Fiber Supported Liquid Membrane for Phenol Extraction from Coal Gasification Wastewater
The extraction and recycling of phenol from high concentration coal gasification wastewater has been studied using polypropylene (PP) hollow fiber membrane and polyvinylidene fluoride (PVDF) hollow fiber membrane as liquid membrane support, the mixture of tributyl phosphate (TBP) and kerosene as liquid membrane phase, and sodium hydroxide as stripping agent in the process of extraction. The experiments investigated the effect of the operating conditions of the hollow fiber supported liquid membrane, such as aqueous phase temperature and the connection forms of membrane modules, on the extraction efficiency of phenol from high concentration coal gasification wastewater. The conclusions obtained from lab scale experiments provided guidance for scale-up experiments. So, in the scale-up experiments, three membrane modules connected in parallel, then three membrane modules connected in series were used to increase the treatment capacity and improve the treatment effect, under the operating conditions of wastewater temperature 20 ËšC, PH 7.5~8.1, flow rate 100 L/h and the concentration of stripping phase 0.1 mol/L, stripping phase flow rate 50 L/h, the extraction efficiency of the PP-TBP supported liquid membrane system was 87.02% and the phenol concentration of effluent was 218.14mg/L. And the phenol concentration of effluent met the requirements of further biodegradation treatment
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